An analysis of clinical and geographical metadata of over 75,000 records in the GISAID COVID-19 database

  1. Anjelina Velazquez
  2. Mhealyssah Bustria
  3. Youwen Ouyang
  4. Niema Moshiri

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Abstract

During the SARS-CoV-2 outbreak that caused the coronavirus pandemic it is important now more than ever that scientists and public health officials work side-by-side and use their available resources to track patient information from those that have been affected by the novel coronavirus. The ability to track the disease helps identify possible trends and patterns that can be used by public health officials to make more informed decisions. Tracking data like this may be the key to helping states and countries safely re-open. However, when analyzing large collections of data there is the occurrence of confounding factors such as biases in patient sampling. In this project, a massive collection of COVID-19 data was analyzed, and explored potential biases in patient sampling were explored.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.22.20199497: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.2 Reading the collection of data: Python was used to load the contents of the gzipped file and create a dictionary to hold the patient records.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.09.22.20199497v1 on medRxiv